This disclosure relates to a system and method of identifying a fault condition. More particularly, this disclosure relates to a system and method of identifying fault conditions utilizing limited actual data.
A modern aircraft engine typically includes as few as eight sensors along a gas path. Data from these sensors is utilized to identify specific faults based on different measured data from combinations of the sensors. Some combinations of faults are difficult to distinguish from each other. Further, the relative lack of data is compounded by sensor noise experienced during flight. For this reason, condition based monitoring system are utilized to isolate the location and cause of a fault.
Condition based monitoring systems typically utilize one many different classification algorithms. Classification algorithms include physics based models, empirical neural networks and knowledge based systems. Each classification scheme has strengths and weakness. For example the physics based models rely on a liner relationship between measurements. However, linear relationships cannot account for non-linear measurements such as can be provided by vibration or oil related sensors. Empirical neural networks are prone to overtraining that can cause questionable results if anything changes with regard to a fault conditions and require large amounts of data to provide adequate coverage of all fault conditions. Additionally, knowledge based systems, like the empirical neural networks rely on knowledge gained through experience in operating a particular system or engine and as such is not useful for newer systems.
Accordingly, it is desirable to develop a fault isolation system and method that improves over current methods that is adaptable to different systems and applications.